How To Pass The Input Tensor Of A Model To A Loss Function?

My goal is to create a custom loss function that calculates the loss based on y_true, y_pred and the tensor of the models input layer: import numpy as np from tensorflow import ker

Solution 1:

You can use add_loss to pass external layers to your loss. Here an example:

import numpy as np
from tensorflow import keras as K

def CustomLoss(y_true, y_pred, input_l):
    return K.backend.sum(K.backend.abs(y_true - input_l * y_pred))

input_shape = (16, 16, 1)
n_sample = 10

X = np.random.uniform(0,1, (n_sample,) + input_shape)
y = np.random.uniform(0,1, (n_sample,) + input_shape)

inp = K.layers.Input(input_shape)
dense = K.layers.Dense(16)(inp)
out = K.layers.Dense(1)(dense)

target = K.layers.Input(input_shape)
model = K.Model(inputs=[inp,target], outputs=out)

model.add_loss( CustomLoss( target, out, inp ) )
model.compile(loss=None, optimizer='adam')
model.fit(x=[X,y], y=None, epochs=3)

To use the model in inference mode (removing the target from inputs)

final_model = K.Model(model.input[0], model.output)
final_model.predict(X)